Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Telpiskās un laikiskās karstās vietas analīze× | Lokālā telpiskā autokorelācija× | |
|---|---|---|
| Nozare | Telpiskā analīze | Telpiskā analīze |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1997–2015 | 1995 |
| Autors≠ | Kulldorff (spatial scan statistic); operationalized for time-series bins by Esri (Emerging Hot Spot Analysis) | Luc Anselin |
| Tips≠ | Spatiotemporal cluster detection | Spatial association analysis |
| Pirmavots≠ | Kulldorff, M. (1997). A spatial scan statistic. Communications in Statistics: Theory and Methods, 26(6), 1481–1496. DOI ↗ | Anselin, L. (1995). Local indicators of spatial association — LISA. Geographical Analysis, 27(2), 93–115. DOI ↗ |
| Citi nosaukumi | emerging hot spot analysis, space-time cube hot spot, spatiotemporal hot spot detection, STHA | local spatial association, local SA, LISA methods, local spatial clustering |
| Saistītās≠ | 5 | 6 |
| Kopsavilkums≠ | Space-Time Hot Spot Analysis extends the classic Getis-Ord Gi* statistic across repeated time slices organised in a space-time cube. By testing each location-time bin for statistically significant clustering of high or low values, then examining the sequence of results over time, it identifies whether clusters are new, intensifying, persistent, sporadic, or diminishing — giving analysts a dynamic picture of how hot and cold spots evolve. | Local Spatial Autocorrelation methods decompose global spatial clustering into location-specific statistics, revealing where in a study area significant clustering or dispersion occurs. Each observation receives its own association score and significance value, enabling the detection of spatial hot spots, cold spots, and spatial outliers rather than reporting a single summary statistic. |
| ScholarGateDatu kopa ↗ |
|
|